FL-MGVN: Federated learning for anomaly detection using mixed gaussian variational self-encoding network

• Propose an anomaly detection classification model that incorporates federated learning and mixed Gaussian variational self-coding networks. • Realize anomaly detection based on federated learning, including network attack and sample dissimilarity. • The proposed MGVN network model first constructs a variational self-coder using a mixed gaussian prior to extract features from the input data, and then constructs a deep support vector network with a mixed gaussian variational self-coder. • Verify the multi-classification anomaly detection performance on benchmark datasets such as NSL-KDD, MNIST and Fashion-MNIST. Anomalous data are such data that deviate from a large number of normal data points, which often have negative impacts on various systems. Current anomaly detection technology suffers from low detection accuracy, high false alarm rate and lack of labeled data. Anomaly detection is of great practical importance as an effective means to detect anomalies in the data and provide important support for the normal operation of various systems. In this paper, we propose an anomaly detection classification model that incorporates federated learning and mixed Gaussian variational self-encoding networks, namely MGVN. The proposed MGVN network model first constructs a variational self-encoder using a mixed Gaussian prior to extracting features from the input data, and then constructs a deep support vector network with the mixed Gaussian variational self-encoder to compress the feature space. The MGVN finds the minimum hypersphere to separate the normal and abnormal data and measures the abnormal fraction by calculating the Euclidean distance between the data features and the hypersphere center. Federated learning is finally incorporated with MGVN (FL-MGVN) to effectively address the problems that multiple participants collaboratively train a global model without sharing private data. The experiments are conducted on the benchmark datasets such as NSL-KDD, MNIST and Fashion-MNIST, which demonstrate that the proposed FL-MGVN has higher recognition performance and classification accuracy than other methods. The average AUC on MNIST and Fashion-MNIST reached 0.954 and 0.937, respectively.